Systems and methods for magnetic resonance image reconstruction

ABSTRACT

Systems and methods for magnetic resonance imaging (MRI) are provided. The systems may obtain a sampling pattern associated with an image sequence. The sampling pattern may be associated with a plurality of phase encoding gradient field values. The systems may also obtain k-space data associated with the image sequence using the sampling pattern. The systems may further reconstruct the image sequence based on the k-space data. The sampling pattern may include a plurality of sampling points. Each of the plurality of sampling points may denote a k-space line associated with the k-space data. Each of the plurality of phase encoding gradient field values may correspond to one single sampling point during a time period associated with at least two consecutive images in the image sequence.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation of U.S. application Ser. No.16/241,042, filed on Jan. 7, 2019, which is incorporated herein byreference to its entirety.

TECHNICAL FIELD

This disclosure generally relates to magnetic resonance imaging (MRI)technology, and more particularly, to methods and systems fordetermining a sampling pattern for real-time MRI image reconstruction.

BACKGROUND

Magnetic resonance imaging (MRI) systems are widely used in medicaldiagnose. For example, cardiac magnetic resonance imaging (CMR)techniques are widely used in a routine examination for the heart of apatient. As another example, real-time MRI techniques can continuouslymonitor an object (e.g., a patient) to be scanned by the MRI systems inreal-time and continuously collect multiple consecutive images of theobject during a time period. Currently, an MR cardiac cine technique maybe used to reconstruct an image sequence of the heart by combining thecardiac magnetic resonance imaging (CMR) techniques and real-time MRItechniques. However, the MR cardiac cine techniques currently use anECG-gating tecnique or a segmented acquirsition techique to collectingMR signals, which needs a patient to hold a breath for a time period andcan not be applied for a patient with arrhythmia. Thus, it is desirableto provide systems and methods for real-time MR image reconstructionwith improved sampling.

SUMMARY

According to an aspect of the present disclosure, a system for magneticresonance imaging (MRI) is provided. The system may include at least onestorage device storing executable instructions, and at least oneprocessor in communication with the at least one storage device. Whenexecuting the executable instructions, the at least one processor mayperform the following operations. The at least one processor may obtaina sampling pattern associated with an image sequence. The samplingpattern may be associated with a plurality of phase encoding gradientfield values. The at least one processor may also obtain k-space dataassociated with the image sequence using the sampling pattern. The atleast one processor may also reconstruct the image sequence based on thek-space data. The sampling pattern may include a plurality of samplingpoints. Each of the plurality of sampling points may denote a k-spaceline associated with the k-space data. Each of the plurality of phaseencoding gradient field values may correspond to one single samplingpoint during a time period associated with at least two consecutiveimages in the image sequence.

In some embodiments, the sampling pattern may be pseudo-random in ak-space direction.

In some embodiments, at least one segment of the sampling pattern alonga phase encoding dimension may not repeat itself in the time period.

In some embodiments, locations of the plurality of sampling points onthe sampling pattern may be arranged periodically along a time dimensionassociated with the sampling pattern.

In some embodiments, the sampling pattern may include one or more blocksarranged in a two-dimensional space defined by a time dimension and aphase encoding dimension. Each of the one or more blocks may include apre-determined number of sampling points. The pre-determined number ofsampling points in each block may correspond to different phase encodinggradient field values and time values.

In some embodiments, the sampling pattern may include at least twosegments along a phase encoding dimension of the sampling pattern. Phaseencoding gradient field values associated with each of the at least twosegments may correspond to a same number of sampling points.

In some embodiments, each of the phase encoding gradient field valuesassociated with each of the at least two segments may correspond to onesingle sampling point during a same time period.

In some embodiments, the at least two segments may include a firstsegment and a second segment. A phase encoding gradient field valueassociated with the first segment may correspond to one single samplingpoint during a first time period. A phase encoding gradient field valueassociated with the second segment may correspond to one single samplingpoint during a second time period different from the first time period.

In some embodiments, each of the at least two segments may include oneor more blocks arranged along a time dimension of the sampling pattern.Each of one or more blocks in each of the at least two segments mayinclude a pre-determined number of sampling points. The pre-determinednumber of sampling points in each block may correspond to differentphase encoding gradient field values and time values.

In some embodiments, the pre-determined number of sampling points oneach of the one or more blocks in each of the at least two segments maybe different.

In some embodiments, locations of sampling points on each block in atleast one of the at least two segments may be the same or different.

In some embodiments, the image sequence may include a plurality ofconsecutive images. To obtain k-space data associated with the imagesequence using the sampling pattern, the at least one processor mayfurther identify a plurality of sampling trajectories from the targetsampling pattern. Each of the plurality of sampling trajectories maycorrespond to one of the plurality of consecutive images. Each of theplurality of sampling trajectories may include a plurality of samplingpoints arranged in a time sequence. The at least one processor may alsodetermine a pulse sequence based on the plurality of samplingtrajectories. The at least one processor may also collect MR signalsbased on the pulse sequence. The at least one processor may alsodetermine the k-space data associated with the image sequence based onthe MR signals.

In some embodiments, a number of the plurality of sampling points oneach of the plurality of sampling trajectories may be the same.

In some embodiments, phase encoding gradient field values correspondingto the plurality of sampling points on at least one of the plurality ofsampling trajectories may vary in a descending order or an ascendingorder.

In some embodiments, directions of sampling trajectories with respect totwo neighboring images may be different. A direction of a samplingtrajectory may be defined by a change of phase encoding gradient fieldvalues corresponding to the plurality of sampling points.

In some embodiments, the sampling trajectories with respect to twoneighboring images may share a sampling point at a phase coding gradientfield value. The phase coding gradient field value may be a maximum orminimum of the plurality of phase encoding gradient field valuesassociated with the sampling pattern.

In some embodiments, to reconstruct the image sequence based on thek-space data, the at least one processor may further average at leastone portion of the k-space data along the time dimension of the samplingpattern to obtain reference k-space data associated with the imagesequence. The at least one processor may also reconstruct the imagesequence based on the reference k-space data.

According to another aspect of the present disclosure, a method formagnetic resonance imaging (MRI) implemented on a computing apparatus isprovided. The computing apparatus may include at least one processor andat least one storage device. The method may include obtaining a samplingpattern associated with an image sequence. The sampling pattern may beassociated with a plurality of phase encoding gradient field values. Themethod may also include obtaining k-space data associated with the imagesequence using the sampling pattern. The method may further includereconstructing the image sequence based on the k-space data. Thesampling pattern may include a plurality of sampling points. Each of theplurality of sampling points may denote a k-space line associated withthe k-space data. Each of the plurality of phase encoding gradient fieldvalues may correspond to one single sampling point during a time periodassociated with at least two consecutive images in the image sequence.

According to another aspect of the present disclosure, a non-transitorycomputer-readable medium storing at least one set of instructions isprovided. When executed by at least one processor, the at least one setof instructions may direct the at least one processor to perform thefollowing operations. The at least one processor may obtain a samplingpattern associated with an image sequence. The sampling pattern beingassociated with a plurality of phase encoding gradient field values. Theat least one processor may also obtain k-space data associated with theimage sequence using the sampling pattern. The at least one processormay also reconstruct the image sequence based on the k-space data. Thesampling pattern may include a plurality of sampling points. Each of theplurality of sampling points may denote a k-space line associated withthe k-space data. Each of the plurality of phase encoding gradient fieldvalues may correspond to one single sampling point during a time periodassociated with at least two consecutive images in the image sequence.

According to another aspect of the present disclosure, a system formagnetic resonance imaging (MRI) is provided. The system may include atleast one storage device storing executable instructions, and at leastone processor in communication with the at least one storage device.When executing the executable instructions, the at least one processormay perform the following operations. The at least one processor maydetermine one or more of blocks associated with a plurality of phaseencoding gradient field values. Each of the one or more of blocks may bedenoted by a square array including a plurality of rows of points. Eachof the plurality of rows of points may be associated with one of theplurality of phase encoding gradient field values. The at least oneprocessor may also determine a plurality of first sampling points fromeach of the one or more of blocks. Each of the plurality of phaseencoding gradient field values may correspond to one single of theplurality of first sampling points. The at least one processor may alsodetermine a sampling pattern based on the plurality of first samplingpoints in each of the one or more of blocks. The at least one processormay also obtain k-space data associated with the image sequence usingthe sampling pattern. The at least one processor may also reconstructthe image sequence based on the k-space data.

According to another aspect of the present disclosure, a system formagnetic resonance imaging (MRI) is provided. The system may include atleast one storage device storing executable instructions, and at leastone processor in communication with the at least one storage device.When executing the executable instructions, the at least one processormay perform the following operations. The at least one processor mayobtain a preliminary sampling pattern associated with an image sequence.The preliminary sampling pattern may be defined by a phase encodingdimension and a time dimension. The at least one processor may alsoclassify the preliminary sampling pattern into one or more segmentsalong the phase encoding dimension. The at least one processor may alsoclassify each of the one or more segments into a plurality of blocks.Each of the one or more of blocks may be denoted by a square arrayincluding a plurality of rows of points. The at least one processor mayalso determine a plurality of sampling points from each row of theplurality of rows of points in each of the plurality of blocks. Theplurality of sampling points may be corresponding to different timevalues. The at least one processor may also determine a target samplingpattern including the plurality of sampling points in the each of theplurality of blocks. Each of the plurality of sampling points mayrepresent a readout line along a frequency encoding direction. The atleast one processor may also obtain k-space data associated with theimage sequence using the target sampling pattern. The at least oneprocessor may also reconstruct images in the image sequence based on thek-space data.

Additional features will be set forth in part in the description whichfollows, and in part will become apparent to those skilled in the artupon examination of the following and the accompanying drawings or maybe learned by production or operation of the examples. The features ofthe present disclosure may be realized and attained by practice or useof various aspects of the methodologies, instrumentalities, andcombinations set forth in the detailed examples discussed below.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is further described in terms of exemplaryembodiments. These exemplary embodiments are described in detail withreference to the drawings. These embodiments are non-limiting exemplaryembodiments, in which like reference numerals represent similarstructures throughout the several views of the drawings, and wherein:

FIG. 1 is a schematic diagram illustrating an exemplary MRI systemaccording to some embodiments of the present disclosure;

FIG. 2 is a schematic diagram illustrating hardware and/or softwarecomponents of an exemplary computing device on which the processingdevice may be implemented according to some embodiments of the presentdisclosure;

FIG. 3 is a schematic diagram illustrating hardware and/or softwarecomponents of an exemplary mobile device according to some embodimentsof the present disclosure;

FIG. 4 is a block diagram illustrating an exemplary processing device120 according to some embodiments of the present disclosure;

FIG. 5 is a flowchart illustrating an exemplary process forreconstructing an image sequence according to some embodiments of thepresent disclosure;

FIG. 6 is a flowchart illustrates an exemplary process forreconstructing an image sequence according to a sampling patternaccording to some embodiments of the present disclosure;

FIG. 7 is a schematic diagram illustrating exemplary blocks according tosome embodiments of the present disclosure;

FIG. 8 is a schematic diagram illustrating an exemplary sampling patternaccording to some embodiments of the present disclosure; and

FIG. 9 is a schematic diagram illustrating exemplary samplingtrajectories associated with one portion of an image sequence accordingto some embodiments of the present disclosure.

DETAILED DESCRIPTION

The following description is presented to enable any person skilled inthe art to make and use the present disclosure and is provided in thecontext of a particular application and its requirements. Variousmodifications to the disclosed embodiments will be readily apparent tothose skilled in the art, and the general principles defined herein maybe applied to other embodiments and applications without departing fromthe spirit and scope of the present disclosure. Thus, the presentdisclosure is not limited to the embodiments shown, but is to beaccorded the widest scope consistent with the claims.

It will be understood that the term “system,” “engine,” “unit,”“module,” and/or “block” used herein are one method to distinguishdifferent components, elements, parts, section or of different level inascending order. However, the terms may be displaced by anotherexpression if they may achieve the same purpose.

Generally, the word “module,” “unit,” or “block,” as used herein, refersto logic embodied in hardware or firmware, or to a collection ofsoftware instructions. A module, a unit, or a block described herein maybe implemented as software and/or hardware and may be stored in any typeof non-transitory computer-readable medium or another storage device. Insome embodiments, a software module/unit/block may be compiled andlinked into an executable program. It will be appreciated that softwaremodules can be callable from other modules/units/blocks or fromthemselves, and/or may be invoked in response to detected events orinterrupts. Software modules/units/blocks configured for execution oncomputing devices (e.g., processor 220 as illustrated in FIG. 2) may beprovided on a computer-readable medium, such as a compact disc, adigital video disc, a flash drive, a magnetic disc, or any othertangible medium, or as a digital download (and can be originally storedin a compressed or installable format that needs installation,decompression, or decryption prior to execution). Such software code maybe stored, partially or fully, on a storage device of the executingcomputing device, for execution by the computing device. Softwareinstructions may be embedded in firmware, such as an ErasableProgrammable Read Only Memory (EPROM). It will be further appreciatedthat hardware modules/units/blocks may be included in connected logiccomponents, such as gates and flip-flops, and/or can be included ofprogrammable units, such as programmable gate arrays or processors. Themodules/units/blocks or computing device functionality described hereinmay be implemented as software modules/units/blocks, but may berepresented in hardware or firmware. In general, themodules/units/blocks described herein refer to logicalmodules/units/blocks that may be combined with othermodules/units/blocks or divided into sub-modules/sub-units/sub-blocksdespite their physical organization or storage. The description may beapplicable to a system, an engine, or a portion thereof.

The terminology used herein is for the purpose of describing particularexample embodiments only and is not intended to be limiting. As usedherein, the singular forms “a,” “an,” and “the” may be intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprise,”“comprises,” and/or “comprising,” “include,” “includes,” and/or“including,” when used in this specification, specify the presence ofstated features, integers, steps, operations, elements, and/orcomponents, but do not preclude the presence or addition of one or moreother features, integers, steps, operations, elements, components,and/or groups thereof.

These and other features, and characteristics of the present disclosure,as well as the methods of operation and functions of the relatedelements of structure and the combination of parts and economies ofmanufacture, may become more apparent upon consideration of thefollowing description with reference to the accompanying drawings, allof which form a part of this disclosure. It is to be expresslyunderstood, however, that the drawings are for the purpose ofillustration and description only and are not intended to limit thescope of the present disclosure. It is understood that the drawings arenot to scale.

The flowcharts used in the present disclosure illustrate operations thatsystems implement according to some embodiments in the presentdisclosure. It is to be expressly understood, the operations of theflowchart may be implemented not in order. Conversely, the operationsmay be implemented in inverted order, or simultaneously. Moreover, oneor more other operations may be added to the flowcharts. One or moreoperations may be removed from the flowcharts.

The present disclosure relates to methods and systems for real-time MRIimage reconstruction. The method may include determining a samplingpattern associated with an image sequence, the sampling pattern beingassociated with a plurality of phase encoding gradient field values. Theimage sequence may correspond to a plurality of consecutive images. Insome embodiments, the sampling pattern may include a plurality of blocksarranged along a phase encoding dimension and a time dimension. Each ofthe plurality of blocks may be associated with one portion of theplurality of phase encoding gradient field values. Each of one portionof the plurality of phase encoding gradient field values may correspondto one single sampling point denoting a k-space line. The method mayalso include obtaining k-space data associated with the image sequenceusing the sampling pattern. In some embodiments, the method may includereconstructing the image sequence based on the k-space data.

Accordingly, each of the plurality of phase encoding gradient fieldvalues associated with the image sequence may be not repeated to beapplied for sampling MR signals during a time period. In other words,the sampling count corresponding to each of the plurality of phaseencoding gradient field values applied for sampling MR signals may beuniform which may improve sampling efficiency.

FIG. 1 is a schematic diagram illustrating an exemplary MRI system 100(also referred to as imaging system 100 herein) according to someembodiments of the present disclosure. As illustrated, the MRI system100 may include an MR scanner (or referred to as an MRI scanner) 110, aprocessing device 120, a storage device 130, one or more terminals 140,and a network 150. The components in the MRI system 100 may be connectedin one or more of various ways. Merely by way of example, as illustratedin FIG. 1, the MR scanner 110 may be connected to the processing device120 through the network 150. As another example, the MR scanner 110 maybe connected with the processing device 120 directly as indicated by thebi-directional arrow in dotted lines linking the MR scanner 110 and theprocessing device 120. As a further example, the storage device 130 maybe connected with the processing device 120 directly (not shown inFIG. 1) or through the network 150. As still a further example, one ormore terminal(s) 140 may be connected with the processing device 120directly (as indicated by the bi-directional arrow in dotted lineslinking the terminal(s) 140 and the processing device 120) or throughthe network 150.

The MR scanner 110 may scan a subject or a portion thereof that islocated within its detection region and generate MR signals relating tothe (part of) subject. In the present disclosure, the terms “subject”and “object” are used interchangeably. In some embodiments, the subjectmay include a body, a substance, or the like, or a combination thereof.In some embodiments, the subject may include a specific portion of abody, such as the head, the thorax, the abdomen, or the like, or acombination thereof. In some embodiments, the subject may include aspecific organ, such as the heart, the esophagus, the trachea, thebronchus, the stomach, the gallbladder, the small intestine, the colon,the bladder, the ureter, the uterus, the fallopian tube, etc. The MRscanner 110 may include a magnet assembly, a gradient coil assembly, anda radiofrequency (RF) coil assembly.

The magnet assembly may generate a first magnetic field (also referredto as a main magnetic field) for polarizing the subject to be scanned.The magnet assembly may include a permanent magnet, a superconductingelectromagnet, a resistive electromagnet, etc.

The gradient coil assembly may generate a second magnetic field. Thegradient coil assembly may include X-gradient coils, Y-gradient coils,and Z-gradient coils. The gradient coil assembly may be powered on byapplying one or more pulses to generate second magnetic fields in the Xdirection, the Y direction, and the Z direction, respectively. The mainmagnetic field may superpose with the second magnetic field gradients toform gradient magnetic fields in the X direction (Gx), the Y direction(Gy), and the Z direction (Gz) respectively to encode the spatialinformation of the subject. In some embodiments, the X direction may bedesignated as a frequency encoding direction, while the Y direction maybe designated as a phase encoding directionphase encoding dimension. Insome embodiments, Gx may be used for frequency encoding or signalreadout, generally referred to as frequency encoding gradient or readoutgradient. In some embodiments, Gy may be used for phase encoding,generally referred to as phase encoding gradient. In some embodiments,Gz may be used for slice selection for obtaining 2D k-space data. Insome embodiments, Gz may be used for phase encoding for obtaining 3Dk-space data.

The RF coil assembly may include a plurality of RF coils. The RF coilsmay include one or more RF transmit coils and/or one or more RF receivercoils. The RF transmit coil(s) may transmit RF pulses to the subject.Under the coordinated action of the main magnetic field, the gradientmagnetic field, and the RF pulses, MR signals relating to the subjectmay be generated according to one or more pulse sequences. The MRsignals may also be referred to as echo signals. Further, the MR signalsmay be processed to fill a k-space to obtain the k-space data based on asampling technique. Exemplary sampling techniques may include aCartesian sampling technique, a spiral sampling technique, a radialsampling technique, a Z-sampling technique, an undersampling technique,etc. The k-space may include a two-dimension (2D) k-space, athree-dimension (3D) k-space, etc. The RF receiver coils may acquire MRsignals from the subject according to the one or more pulse sequences. Apulse sequence may be defined by imaging parameters and arrangementassociated with the image parameters in time sequence. In someembodiments, the imaging parameters may include parameters relating toan RF pulse (e.g., the number of excitations (NEX), a bandwidth, etc.)emitted by the RF coil, parameters relating to gradient fields generatedby the gradients coil (e.g., a gradient direction, a time for applying agradient field, an intensity for applying a gradient gradient, aduration for applying a gradient, etc.), and parameters relating to MRsignals (e.g., an echo time (TE), an echo train length (ETL), a spinecho type, the number of phases), etc. Exemplary pulse sequences mayinclude a spin echo sequence, a gradient echo sequence, a diffusionsequence, an inversion recovery sequence, or the like, or a combinationthereof. For example, the spin echo sequence may include a fast spinecho (FSE), a turbo spin echo (TSE), a rapid acquisition with relaxationenhancement (RARE), a half-Fourier acquisition single-shot turbospin-echo (HASTE), a turbo gradient spin echo (TGSE), or the like, or acombination thereof.

A pulse sequence may be set by a user or a default setting of the MRIsystem 100. For example, the gradient echo sequence (e.g., a T1WIgradient echo sequence) may be applied for a heart scan. In someembodiments, at least one portion of a pulse sequence may be designedbased on a sampling pattern. For example, the parameters relating togradient fields of a pulse sequence (e.g., a time for applying a phaseencoding gradient field, an intensity for applying a phase encodinggradient field, a duration for applying a phase encoding gradientgradient field, etc.) may be determined based on the sampling pattern.The sampling pattern may include a plurality of sampling pointsassociated with a plurality of phase encoding gradient field values.Each of the plurality of phase encoding gradient field valuescorresponding to one single sampling point during a time period. Each ofthe plurality of sampling points may be defined by a phase encodinggradient field value and a time value. A time for applying a phaseencoding gradient field and/or an intensity for applying a phaseencoding gradient field may be determined according to a phase encodinggradient field value and a time value associated with a sampling pointin the sampling pattern. In some embodiments, the sampling pattern mayinclude a plurality of blocks arranged in a two-dimensional spacedefined by a phase encoding dimension (i.e., phase encodingdirectionphase encoding dimension) and a time dimension (i.e., timedirectiontime dimension). Each of the plurality of blocks may beassociated with one portion of the plurality of phase encoding gradientfield values. Each of one portion of the plurality of phase encodinggradient field values may correspond to one single sampling pointdenoting a k-space line (i.e., readout line). More descriptions for thesampling pattern may be found elsewhere in the present disclosure (e.g.,FIGS. 5-8, and the descriptions thereof).

In some embodiments, the MR scanner 110 may include an analog-to-digitalconverter (ADC) (not shown in FIG. 1). The analog-to-digital convertermay convert MR signals received by one or more RF receiver coils into MRimage data. The analog-to-digital converter may be a direct-conversionADC, a successive-approximation ADC, a ramp-compare ADC, a WilkinsonADC, an integrating ADC, a delta-encoded ADC, a pipeline ADC, asigma-delta ADC, or the like, or a combination thereof.

The processing device 120 may process data and/or information obtainedfrom the MR scanner 110, the terminal(s) 140, and/or the storage device130. For example, the processing device 120 may determine a samplingpattern associated with an image sequence including a plurality ofconsecutive images (i.e., images). As another example, the processingdevice 120 may obtain k-space data associated with the image sequenceusing the sampling pattern. As still an example, the processing device120 may reconstruct the image sequence based on the k-space data. Insome embodiments, the reconstructed image may be transmitted to theterminal(s) 140 and displayed on one or more display devices in theterminal(s) 140. In some embodiments, the processing device 120 may be asingle server or a server group. The server group may be centralized ordistributed. In some embodiments, the processing device 120 may be localor remote. For example, the processing device 120 may access informationand/or data stored in the MR scanner 110, the terminal(s) 140, and/orthe storage device 130 via the network 150. As another example, theprocessing device 120 may be directly connected with the MR scanner 110,the terminal(s) 140, and/or the storage device 130 to access storedinformation and/or data. In some embodiments, the processing device 120may be implemented on a cloud platform. Merely by way of example, thecloud platform may include a private cloud, a public cloud, a hybridcloud, a community cloud, a distributed cloud, an inter-cloud, amulti-cloud, or the like, or any combination thereof. In someembodiments, the processing device 120 may be implemented on a computingdevice 200 having one or more components illustrated in FIG. 2 in thepresent disclosure.

The storage device 130 may store data and/or instructions. In someembodiments, the storage device 130 may store data obtained from theterminal(s) 140 and/or the processing device 120. For example, thestorage device 130 may store MR signals obtained from the MR scanner 110and k-space data associated with the MR signals. As another example, thestorage device 130 may store a sampling pattern including differentsampling trajectories. As still another example, the storage device 130may store one or more MR image reconstruction algorithms as describedelsewhere in the present disclosure. In some embodiments, the storagedevice 130 may store data and/or instructions that the processing device120 may execute or use to perform exemplary methods described in thepresent disclosure. In some embodiments, the storage device 130 mayinclude a mass storage device, a removable storage device, a volatileread-and-write memory, a read-only memory (ROM), or the like, or anycombination thereof. Exemplary mass storage may include a magnetic disk,an optical disk, a solid-state drive, etc. Exemplary removable storagemay include a flash drive, a floppy disk, an optical disk, a memorycard, a zip disk, a magnetic tape, etc. Exemplary volatileread-and-write memories may include a random access memory (RAM).Exemplary RAM may include a dynamic RAM (DRAM), a double date ratesynchronous dynamic RAM (DDR SDRAM), a static RAM (SRAM), a thyristorRAM (T-RAM), and a zero-capacitor RAM (Z-RAM), etc. Exemplary ROM mayinclude a mask ROM (MROM), a programmable ROM (PROM), an erasableprogrammable ROM (PEROM), an electrically erasable programmable ROM(EEPROM), a compact disk ROM (CD-ROM), and a digital versatile disk ROM,etc. In some embodiments, the storage device 130 may be implemented on acloud platform. Merely by way of example, the cloud platform may includea private cloud, a public cloud, a hybrid cloud, a community cloud, adistributed cloud, an inter-cloud, a multi-cloud, or the like, or anycombination thereof.

In some embodiments, the storage device 130 may be connected with thenetwork 150 to communicate with one or more components of the MRI system100 (e.g., the processing device 120, the terminal(s) 140, etc.). One ormore components of the MRI system 100 may access the data orinstructions stored in the storage device 130 via the network 150. Insome embodiments, the storage device 130 may be directly connected withor communicate with one or more components of the MRI system 100 (e.g.,the processing device 120, the terminal(s) 140, etc.). In someembodiments, the storage device 130 may be part of the processing device120.

The terminal(s) 140 may include a mobile device 140-1, a tablet computer140-2, a laptop computer 140-3, or the like, or any combination thereof.In some embodiments, the mobile device 140-1 may include a smart homedevice, a wearable device, a smart mobile device, a virtual realitydevice, an augmented reality device, or the like, or any combinationthereof. In some embodiments, the smart home device may include a smartlighting device, a control device of an intelligent electricalapparatus, a smart monitoring device, a smart television, a smart videocamera, an interphone, or the like, or any combination thereof. In someembodiments, the wearable device may include a smart bracelet, smartfootgear, a pair of smart glasses, a smart helmet, a smartwatch, smartclothing, a smart backpack, a smart accessory, or the like, or anycombination thereof. In some embodiments, the smart mobile device mayinclude a smartphone, a personal digital assistant (PDA), a gamingdevice, a navigation device, a point of sale (POS) device, or the like,or any combination thereof. In some embodiments, the virtual realitydevice and/or the augmented reality device may include a virtual realityhelmet, a virtual reality glass, a virtual reality patch, an augmentedreality helmet, an augmented reality glass, an augmented reality patch,or the like, or any combination thereof. For example, the virtualreality device and/or the augmented reality device may include a GoogleGlass, an Oculus Rift, a Hololens, a Gear VR, etc. In some embodiments,the terminal(s) 140 may remotely operate the MR scanner 110. In someembodiments, the terminal(s) 140 may operate the MR scanner 110 via awireless connection. In some embodiments, the terminal(s) 140 mayreceive information and/or instructions inputted by a user, and send thereceived information and/or instructions to the MR scanner 110 or theprocessing device 120 via the network 150. In some embodiments, theterminal(s) 140 may receive data and/or information from the processingdevice 120. In some embodiments, the terminal(s) 140 may be part of theprocessing device 120. In some embodiments, the terminal(s) 140 may beomitted.

In some embodiments, the terminal(s) 140 may send and/or receiveinformation for MR image reconstruction to the processing device 120 viaa user interface. The user interface may be in the form of anapplication for MR image reconstruction implemented on the terminal(s)140. The user interface implemented on the terminal(s) 140 may beconfigured to facilitate communication between a user and the processingdevice 120. In some embodiments, a user may input a request for MR imagereconstruction via the user interface implemented on a terminal 140. Theterminal(s) 140 may send the request for MR image reconstruction to theprocessing device 120 for reconstructing an MR image sequence based onk-space data obtained based on sampling pattern as described elsewherein the present disclosure (e.g., FIGS. 5-8 and the descriptionsthereof). In some embodiments, the user may input and/or adjustparameters (e.g., the number or count of sampling points, the size,etc.) of the sampling pattern via the user interface. In someembodiments, the user interface may facilitate the presentation ordisplay of information and/or data (e.g., a signal) relating to MR imagereconstruction received from the processing device 120. For example, theinformation and/or data may include a result generated by the processingdevice 120 in an image reconstruction. For example, the result mayinclude one or more images (e.g., 2D images, 3D images, etc.), one ormore data figures, one or more words, one or more digits, one or moremodels for MR image reconstruction, parameters used in such imagereconstruction, etc. In some embodiments, the information and/or datamay be further configured to cause the terminal(s) 140 to display theresult to the user.

The network 150 may include any suitable network that can facilitate theexchange of information and/or data for the MRI system 100. In someembodiments, one or more components of the MRI system 100 (e.g., the MRscanner 110, the terminal(s) 140, the processing device 120, or thestorage device 130) may communicate information and/or data with one ormore other components of the MRI system 100 via the network 150. Forexample, the processing device 120 may obtain MR signals from the MRscanner 110 via the network 150. As another example, the processingdevice 120 may obtain user instructions from the terminal(s) 140 via thenetwork 150. In some embodiments, the network 150 may be any type ofwired or wireless network, or a combination thereof. The network 150 maybe and/or include a public network (e.g., the Internet), a privatenetwork (e.g., a local area network (LAN), a wide area network (WAN)),etc.), a wired network (e.g., an Ethernet network), a wireless network(e.g., an 802.11 network, a Wi-Fi network, etc.), a cellular network(e.g., a Long Term Evolution (LTE) network), an image relay network, avirtual private network (“VPN”), a satellite network, a telephonenetwork, routers, hubs, switches, server computers, and/or anycombination thereof. Merely by way of example, the network 150 mayinclude a cable network, a wireline network, a fiber-optic network, atelecommunications network, an intranet, a wireless local area network(WLAN), a metropolitan area network (MAN), a public telephone switchednetwork (PSTN), a Bluetooth™ network, a ZigBee™ network, a near fieldcommunication (NFC) network, or the like, or any combination thereof. Insome embodiments, the network 150 may include one or more network accesspoints. For example, the network 150 may include wired and/or wirelessnetwork access points such as base stations and/or internet exchangepoints through which one or more components of the MRI system 100 may beconnected with the network 150 to exchange data and/or information.

The network 150 may include any suitable network that can facilitate theexchange of information and/or data for the MRI system 100. In someembodiments, one or more components of the MRI system 100 (e.g., the MRscanner 110, the terminal(s) 140, the processing device 120, the storagedevice 130, etc.) may transmit or receive information and/or data withone or more other components of the MRI system 100 via the network 150.For example, the processing device 120 may obtain image data from the MRscanner 110 via the network 150. As another example, the processingdevice 120 may obtain user instructions from the terminal(s) 140 via thenetwork 150. The network 150 may be and/or include a public network(e.g., the Internet), a private network (e.g., a local area network(LAN), a wide area network (WAN)), etc.), a wired network (e.g., anEthernet network), a wireless network (e.g., an 802.11 network, a Wi-Finetwork, etc.), a cellular network (e.g., a Long Term Evolution (LTE)network), an image relay network, a virtual private network (“VPN”), asatellite network, a telephone network, routers, hubs, switches, servercomputers, and/or any combination thereof. Merely by way of example, thenetwork 150 may include a cable network, a wireline network, afiber-optic network, a telecommunications network, an intranet, awireless local area network (WLAN), a metropolitan area network (MAN), apublic telephone switched network (PSTN), a Bluetooth™ network, aZigBee™ network, a near field communication (NFC) network, or the like,or any combination thereof. In some embodiments, the network 150 mayinclude one or more network access points. For example, the network 150may include wired and/or wireless network access points such as basestations and/or internet exchange points through which one or morecomponents of the MRI system 100 may be connected with the network 150to exchange data and/or information.

FIG. 2 is a schematic diagram illustrating hardware and/or softwarecomponents of an exemplary computing device 200 on which the processingdevice 120 may be implemented according to some embodiments of thepresent disclosure. As illustrated in FIG. 2, the computing device 200may include a processor 210, a storage 220, an input/output (I/O) 230,and a communication port 240.

The processor 210 may execute computer instructions (program code) andperform functions of the processing device 120 in accordance withtechniques described herein. The computer instructions may include, forexample, routines, programs, objects, components, signals, datastructures, procedures, modules, and functions, which perform particularfunctions described herein. For example, the processor 210 may processdata obtained from the MR scanner 110, the terminal(s) 140, the storagedevice 130, and/or any other component of the imaging system 100.Specifically, the processor 210 may process one or more measured datasets obtained from the MR scanner 110. For example, the processor 210may reconstruct an image based on the data set(s). In some embodiments,the reconstructed image may be stored in the storage device 130, thestorage 220, etc. In some embodiments, the reconstructed image may bedisplayed on a display device by the I/O 230. In some embodiments, theprocessor 210 may perform instructions obtained from the terminal(s)140. In some embodiments, the processor 210 may include one or morehardware processors, such as a microcontroller, a microprocessor, areduced instruction set computer (RISC), an application specificintegrated circuits (ASICs), an application-specific instruction-setprocessor (ASIP), a central processing unit (CPU), a graphics processingunit (GPU), a physics processing unit (PPU), a microcontroller unit, adigital signal processor (DSP), a field programmable gate array (FPGA),an advanced RISC machine (ARM), a programmable logic device (PLD), anycircuit or processor capable of executing one or more functions, or thelike, or any combinations thereof.

Merely for illustration, only one processor is described in thecomputing device 200. However, it should be noted that the computingdevice 200 in the present disclosure may also include multipleprocessors. Thus operations and/or method steps that are performed byone processor as described in the present disclosure may also be jointlyor separately performed by the multiple processors. For example, if inthe present disclosure the processor of the computing device 200executes both operation A and operation B, it should be understood thatoperation A and operation B may also be performed by two or moredifferent processors jointly or separately in the computing device 200(e.g., a first processor executes operation A and a second processorexecutes operation B, or the first and second processors jointly executeoperations A and B).

The storage 220 may store data/information obtained from the MR scanner110, the terminal(s) 140, the storage device 130, or any other componentof the imaging system 100. In some embodiments, the storage 220 mayinclude a mass storage device, a removable storage device, a volatileread-and-write memory, a read-only memory (ROM), or the like, or anycombination thereof. For example, the mass storage may include amagnetic disk, an optical disk, a solid-state drive, etc. The removablestorage may include a flash drive, a floppy disk, an optical disk, amemory card, a zip disk, a magnetic tape, etc. The volatileread-and-write memory may include a random access memory (RAM). The RAMmay include a dynamic RAM (DRAM), a double date rate synchronous dynamicRAM (DDR SDRAM), a static RAM (SRAM), a thyristor RAM (T-RAM), and azero-capacitor RAM (Z-RAM), etc. The ROM may include a mask ROM (MROM),a programmable ROM (PROM), an erasable programmable ROM (PEROM), anelectrically erasable programmable ROM (EEPROM), a compact disk ROM(CD-ROM), and a digital versatile disk ROM, etc. In some embodiments,the storage 220 may store one or more programs and/or instructions toperform exemplary methods described in the present disclosure. Forexample, the storage 220 may store a program for the processing device120 for reducing or removing one or more artifacts in an image.

The I/O 230 may input or output signals, data, and/or information. Insome embodiments, the I/O 230 may enable user interaction with theprocessing device 120. In some embodiments, the I/O 230 may include aninput device and an output device. Exemplary input devices may include akeyboard, a mouse, a touch screen, a microphone, or the like, or acombination thereof. Exemplary output devices may include a displaydevice, a loudspeaker, a printer, a projector, or the like, or acombination thereof. Exemplary display devices may include a liquidcrystal display (LCD), a light-emitting diode (LED)-based display, aflat panel display, a curved screen, a television device, a cathode raytube (CRT), or the like, or a combination thereof.

The communication port 240 may be connected with a network (e.g., thenetwork 150) to facilitate data communications. The communication port240 may establish connections between the processing device 120 and theMR scanner 110, the terminal(s) 140, or the storage device 130. Theconnection may be a wired connection, a wireless connection, or acombination of both that enables data transmission and reception. Thewired connection may include an electrical cable, an optical cable, atelephone wire, or the like, or any combination thereof. The wirelessconnection may include Bluetooth, Wi-Fi, WiMax, WLAN, ZigBee, mobilenetwork (e.g., 3G, 4G, 5G, etc.), or the like, or a combination thereof.In some embodiments, the communication port 240 may be a standardizedcommunication port, such as RS232, RS485, etc. In some embodiments, thecommunication port 240 may be a specially designed communication port.For example, the communication port 240 may be designed in accordancewith the digital imaging and communications in medicine (DICOM)protocol.

FIG. 3 is a schematic diagram illustrating hardware and/or softwarecomponents of an exemplary mobile device 300 according to someembodiments of the present disclosure. As illustrated in FIG. 3, themobile device 300 may include a communication platform 310, a display320, a graphics processing unit (GPU) 330, a central processing unit(CPU) 340, an I/O 350, a memory 360, and a storage 390. In someembodiments, any other suitable component, including but not limited toa system bus or a controller (not shown), may also be included in themobile device 300. In some embodiments, a mobile operating system 370(e.g., iOS, Android, Windows Phone, etc.) and one or more applications380 may be loaded into the memory 360 from the storage 390 in order tobe executed by the CPU 340. The applications 380 may include a browseror any other suitable mobile apps for receiving and renderinginformation relating to image processing or other information from theprocessing device 120. User interactions with the information stream maybe achieved via the I/O 350 and provided to the processing device 120and/or other components of the imaging system 100 via the network 150.

To implement various modules, units, and their functionalities describedin the present disclosure, computer hardware platforms may be used asthe hardware platform(s) for one or more of the elements describedherein. The hardware elements, operating systems and programminglanguages of such computers are conventional in nature, and it ispresumed that those skilled in the art are adequately familiar therewithto adapt those technologies to generate an image as described herein. Acomputer with user interface elements may be used to implement apersonal computer (PC) or another type of work station or terminaldevice, although a computer may also act as a server if appropriatelyprogrammed. It is believed that those skilled in the art are familiarwith the structure, programming and general operation of such computerequipment and as a result, the drawings should be self-explanatory.

FIG. 4 is a block diagram illustrating an exemplary processing deviceaccording to some embodiments of the present disclosure. The processingdevice 120 may include an acquisition module 402, a sampling patterndetermination module 404, an image reconstruction module 406, and astorage module 408. At least a portion of the processing device 120 maybe implemented on a computing device as illustrated in FIG. 2 or amobile device as illustrated in FIG. 3.

The acquisition module 402 may obtain data for MR image reconstruction.The data for MR image reconstruction may include image data, a samplingpattern, algorithms for MR image reconstruction, etc. For example, theacquisition module 402 may obtain the image data from the MRI system 100and/or a storage device (e.g., the storage device 130, the storage 220,the storage 390). In some embodiments, the image data may includek-space data (e.g., k-space data associated with an image sequence, animage, etc. The k-space data may be associated with MR signals acquiredby an MR scanner (e.g., the MR scanner 110) scanning a subject (e.g., asubstance, an organ, a tissue, etc.). In some embodiments, the k-spacedata may be generated by filling a k-space using the MR signals. In someembodiments, the acquisition module 402 may send the image data to othermodules and/or units of the processing device 120 for furtherprocessing. For example, the image data may be sent to the storagemodule 408 for storage. For another example, the acquisition module 402may send the image data (e.g., the scanning data) to the imagereconstruction module 406 for reconstructing an image.

The sampling pattern determination module 404 may be configured todetermine a sampling pattern. The sampling pattern may be associatedwith an image sequence and a plurality of phase encoding gradient fieldvalues. When a specific phase encoding gradient field value is appliedfor collecting an MR signal based on the sampling pattern, the specificphase encoding gradient field value may be not applied for collecting MRsignals during a period of time. For example, the sampling patterndetermination module 404 may determine the sampling pattern based on apreliminary sampling pattern. The preliminary sampling pattern may beassociated with the image sequence in a time dimension and the pluralityof phase encoding gradient field values in a phase encoding dimension.The sampling pattern determination module 404 may classify thepreliminary sampling pattern into one or more segments (e.g., a firstsegment, a second segment and a third segments) along the phase encodingdimension. The sampling pattern determination module 404 may classifyeach of the one or more segments into a plurality of blocks including aplurality of sampling points along the time dimension and the phaseencoding dimension. Details (e.g., size of blocks, or locations ofsampling points) of determining the sampling pattern may be foundelsewhere in the present disclosure (e.g., FIG. 5-6 and the descriptionsthereof).

The image reconstruction module 406 may reconstruct an image sequence.

For example, the image reconstruction module 406 may reconstruct aplurality of images included in the image sequence based on the k-spacedata obtained by the acquisition module 402 by using an MRreconstruction technique as described elsewhere in the presentdisclosure. As another example, the image reconstruction module 406 maydetermine a reference readout line may by weighting and/or averaging thek-space data along the time dimension. The reference readout line may beused for reference by the image reconstruction module 406 to reconstructthe image sequence.

The storage module 408 may store information. The information mayinclude programs, software, image reconstruction algorithms, image data,control parameters, processed image data, or the like, or a combinationthereof. For example, the information may include block data (e.g., sizeof a block, locations of sampling points on the block), sampling patterndata (e.g., a count or number of blocks of a sampling pattern, aplurality of sampling trajectories in the sampling pattern), k-spacedata, image sequences, reconstructed images, etc. In some embodiments,the storage module 408 may store one or more programs and/orinstructions that may be executed by the processor(s) of the processingdevice 120 to perform exemplary methods described in this disclosure.For example, the storage module 408 may store program(s) and/orinstruction(s) that can be executed by the processor(s) of theprocessing device 120 to determine one or more blocks, determine asampling pattern, acquire image data, and/or reconstruct an image basedon the image data.

In some embodiments, one or more modules illustrated in FIG. 4 may beimplemented in at least part of the exemplary MRI system 100 asillustrated in FIG. 1. For example, the acquisition module 402, thestorage module 408, the sampling pattern determination module 404,and/or the image reconstruction module 406 may be integrated into aconsole (not shown). Via the console, a user may set parameters fordetermining a sampling pattern, controlling imaging processes,controlling parameters for reconstruction of an image, viewingreconstructed images, etc. In some embodiments, the console may beimplemented via the processing device 120 and/or the terminals 140. Insome embodiments, the sampling pattern determination module 404 may beintegrated into the terminals 140.

In some embodiments, the processing device 120 may not include thesampling pattern determination module 404. One or more sampling patternsdetermined by another device may be stored in the MRI system 100 (e.g.,the storage device 130, the storage 220, the storage 390, the memory360, the storage module 408, etc.) or in an external device accessibleby the processing device 120 via, for example, the network 150. In someembodiments, such a device may include a portion the same as or similarto the sampling pattern determination module 404. In some embodiments,the sampling pattern determination module 404 may store one or moreblocks determined by another device and be accessible by one or morecomponents of the MRI system 100 (e.g., the image reconstruction module406, etc.). In some embodiments, a sampling pattern applicable in thepresent disclosure may be determined by the MRI system 100 (or a portionthereof including, e.g., the processing device 120) or an externaldevice accessible by the MRI system 100 (or a portion thereof including,e.g., the processing device 120) following the processes disclosureherein.

FIG. 5 is a flowchart illustrating an exemplary process forreconstructing an image sequence according to some embodiments of thepresent disclosure. In some embodiments, one or more operations ofprocess 500 illustrated in FIG. 5 may be implemented in the MRI system100 illustrated in FIG. 1. For example, process 500 illustrated in FIG.5 may be stored in the storage device 130 in the form of instructions,and invoked and/or executed by the processing device 120 (e.g., theprocessor 210 of the computing device 200 as illustrated in FIG. 2, theGPU 330 or CPU 340 of the mobile device 300 as illustrated in FIG. 3).

In 502, a sampling pattern associated with an image sequence may bedetermined. Operation 502 may be performed by the sampling patterndetermination module 404. The image sequence may include a plurality ofconsecutive images (also referred to as images) during a time period(e.g., 0.8 s, 0.9 s, 1 s, etc.), such as one or more cardiac cycles.Each of the plurality of consecutive images may correspond to a sub-timeperiod (e.g., 40 ms, 50 ms, etc.) of the time period. The number orcount of the plurality of images in the image sequence may be set by auser or according to a default setting of the MRI system 100. Forexample, the number or count of the plurality of images in the imagesequence may be 12, 16, 20, 24, 36, 48, etc. Each of the plurality ofconsecutive images may have a same size. As used herein, the size of animage may be defined by the number or count of pixels or voxels in theimage. For example, an image may be denoted by a matrix including aplurality of elements (i.e., pixels or voxels). The size of an image maybe denoted as a size of the matrix, such as 256×256, 144×144, etc.

The sampling pattern may be used to acquire encoded MR signalsassociated with k-space data for reconstructing the plurality ofconsecutive images. The MR signals may be encoded by a plurality ofphase encoding gradient fields during a scan. The sampling pattern maybe associated with intensities of the plurality of phase encodinggradient fields (i.e., phase encoding gradient field values) andarrangements thereof in time sequence. Further, the sampling pattern maybe configured to determine intensities of phase encoding gradient fieldsand when the phase encoding gradient fields need to be applied forencoding MR signals associated with k-space corresponding to theplurality of consecutive images in the image sequence.

In some embodiments, the sampling pattern may have a vertical axisdenoting a phase encoding dimension, and a horizontal axis denoting atime dimension. The time dimension may correspond to the plurality ofconsecutive images in the image sequence. Each of the plurality ofconsecutive images in the image sequence may correspond to a specificsub-time period (e.g., 50 ms, 40 ms, etc.) which means each of theplurality of consecutive images may be reconstructed based on MR signalsacquired during the specific sub-time period. The phase encodingdimension may correspond to the plurality of phase encoding gradientfield values. Each phase encoding gradient field value may define anintensity and a direction of a phase encoding gradient field. Theplurality of phase encoding gradient field values may be arrangedsymmetrically along the phase encoding dimension. For example, theplurality of phase encoding gradient field values may be arrangedsymmetrically from +128 to −128 along the phase encoding dimension. As afurther example, a phase encoding gradient field value of +128 may havethe same intensity and an opposite direction of a phase encodinggradient field value of −128. The phase encoding gradient field value of−128 may be designated to be smaller than the phase encoding gradientfield value of +128. The total number of the plurality of phase encodinggradient field values may relate to a size of each image in the imagesequence. Taking the image size of 256×256 as an example, there may be256 phase encoding gradient field values associated with the samplingpattern. The phase encoding gradient field values may range from −128 to+128. Taking the image size of 128×128 as an example, there may be 128phase encoding gradient field values associated with the samplingpattern. The phase encoding gradient field values may range from −64 to+64.

For brevity, the sampling pattern may be denoted as a two-dimensionalgrid (2D grid) which may be described by a Cartesian coordinate. Thevertical axis of the 2D grid may refer to the phase encoding dimension(e.g., ky direction as shown in FIG. 8), and the horizontal axis mayrefer to the time dimension (e.g., t direction as shown in FIG. 8). The2D grid may also be referred to as a k-t grid. The k-t grid may includea plurality of columns of cells (or points) arranged along the timedimension. Each column of the plurality of columns of cells maycorrespond to one image. The k-t grid may include a plurality of rows ofcells arranged along the phase encoding dimension. Each row of theplurality of rows of cells may correspond to a phase encoding gradientfield value.

The sampling pattern may be pseudo-random in a k-space direction (alsoreferred to as a readout direction). The sampling pattern may include aplurality of sampling points. A sampling point herein may represent areadout line (also referred to as a readout line or k-space line) at aspecific phase encoding gradient field value along an orthogonaldirection (not shown) of the phase encoding dimension and the timedimension. A position of each of the plurality of sampling points (i.e.,k-space lines) in the sampling pattern may be defined by a phaseencoding gradient field value and a specific image in the image sequenceor a specific sub-time period associated with the specific image. Theposition of each of the plurality of sampling points in the samplingpattern may satisfy a criterion. The criterion may be that each of theplurality of phase encoding gradient field values associated with thesampling pattern corresponds to one single point during several sub-timeperiods. In other words, at least one segment of the sampling patternmay not repeat itself during the several sub-time periods. Each of theseveral sub-time periods may be associated with several consecutiveimages. In other words, a k-space line in a k-space associated with aphase encoding gradient field value may be sampled only once during atime period (e.g., the several sub-time periods), and is not sampledrepeatedly during the time period (e.g., the several sub-time periods).For example, as shown in FIG. 8, a phase encoding gradient field valueassociated with sampling point S3 corresponds to just one singlesampling point S3 during time period t1, and the time period t1 includes9 sub-time periods associated with 9 images. Each of the plurality ofconsecutive images in the image sequence may correspond to one or moresampling points corresponding to different phase encoding gradient fieldvalues. In some embodiments, the number or count of sampling pointsassociated with each of the plurality of consecutive images may be sameor different. Each of the plurality of phase encoding gradient fieldvalues associated with the image sequence may correspond to one or moresampling points. The number or count of sampling points corresponding toeach of the plurality of phase encoding gradient field values may besame or different. For example, a phase encoding gradient field valuecorresponding to a center region of the sampling pattern may correspondto more sampling points than edge regions of the sampling pattern. Inother words, the sampling density in the center region of the samplingpattern may be greater than that in the edge regions of the samplingpattern. As used herein, the sampling density of a region in thesampling pattern refers to the count of sampling points in a unit areaof the region. In other words, different phase encoding gradient fieldvalues corresponding to one single sampling point may be during a sametime period or different time periods. For example, as shown in FIG. 8,a phase encoding gradient field value associated with sampling point S3corresponds to one single sampling point S3 during time period t1, andthe time period t1 includes 9 sub-time periods associated with 9 images.A phase encoding gradient field value associated with sampling point S4corresponds to one single sampling point S4 during time period t2, andthe time period t2 includes 4 sub-time periods associated with 4 images.In some embodiments, locations of the plurality of sampling points onthe sampling pattern may be arranged periodically along the timedimension associated with the sampling pattern. Locations of samplingpoints along the phase encoding dimension in two cycles may be the same.In other words, phase encoding gradient field values of twocorresponding sampling points in two cycles may be the same. One cyclemay be associated with several images in the image sequence, forexample, 4 images, 6 images, 8 images, etc.

In some embodiments, the sampling pattern may include a plurality ofblocks. The plurality of blocks may be arranged in a two-dimensionalspace defined by the phase encoding dimension and the time dimension.Each of the plurality of blocks may include a pre-determined number ofsampling points associated with a time period and one portion of theplurality of phase encoding gradient field values associated with theimage sequence. For example, if the plurality of phase encoding gradientfield values associated with the image sequence range from −128 to +128,a block may be associated with the one portion of the plurality of phaseencoding gradient field values ranging from +128 to +120, +128 to +120,−128 to −120, etc. A time period associated with a block may beclassified into multiple sub-time periods. Positions of sampling pointsin a block may need to satisfy the criterion. The criterion may be thateach of phase encoding gradient field values associated with a block maycorrespond to one single sampling point and each of the multiplesub-time periods associated with the block may correspond to one singlesampling point. In other words, a block herein may be denoted as asquare array including a plurality of points (or squares) arranged in aplurality of columns and rows. One single point in each of the pluralityof columns of the block may be determined as a sampling point. Onesingle point in each of the plurality of rows of the block may bedetermined as a sampling point. Sampling points in each of the pluralityof columns of the block may be located in different rows of the block.In other words, the sampling points in the block may occupy differentcolumns and different rows. For example, if a position of each of thesampling points in the block is denoted by coordinates including ahorizontal coordinate (e.g., a phase encoding gradient value) and avertical coordinate (e.g., a time value or a sequence number of animage), horizontal coordinates of each of the sampling points aredifferent and the vertical coordinates of each of the sampling pointsare different. Accordingly, when a specific phase encoding gradientfield value is applied for collecting an MR signal, the specific phaseencoding gradient field value may be not applied for collecting MRsignals during a time period (e.g., such as 200 ms, 300 ms, 400 ms,etc.) associated with a block. The sampling points in a block may bedetermined by a user or according to a default setting of the MRI system100. For example, the user may select the sampling points from the blockmanually. As another example, the processing device 120 may select thesampling points from the block randomly.

A block may have a size N×M, (e.g., 4×4, 6×6, 9×9, etc.) denoted by asquare array including a plurality of points. N may refer to a count ofrows in the block relating to phase encoding gradient field values. Mmay refer to a count of columns in the block relating to a time periodassociated the block. The size of a block may be defined by the count ofthe plurality of points in the block. For example, a block with size of4×4 (also referred to as a 4×4 block, e.g., a block 702 (A) or a block704 (B) as shown in FIG. 7) may include 16 points. A block with size of9×9 (also referred to as a 9×9 block, e.g., a block 706 (C) or a block708 (D) as shown in FIG. 7) may include 81 points. Different sizes ofthe block may be associated with different counts (numbers) of phaseencoding gradient field values, include different counts (numbers) ofthe plurality of points, and/or different counts (numbers) of samplingpoints in the different blocks. For example, a 4×4 block (e.g., a block702 (A) or a block 704 (B) as shown in FIG. 7) may be associated with 4phase encoding gradient field values, include 16 points and 4 samplingpoints. As another example, a 9×9 block (e.g., a block 706 (C) or ablock 708 (D) as shown in FIG. 7) may be associated with 9 phaseencoding gradient field values, include 81 points and 9 sampling points.

In some embodiments, the plurality of blocks included in the samplingpattern may include a same size. For the plurality of blocks with thesame size, a count (number) of the plurality of points and the samplingpoints in each of the plurality of blocks may be the same. The count(number) of sampling points correspond to each of the plurality of phaseencoding gradient field values associated with the sampling pattern maybe the same. In some embodiments, blocks in different regions of thesampling pattern may have different sizes. For example, blocks in acenter region of the sampling pattern may have a smaller size than edgeregions of the sampling pattern. In other words, a count of samplingpoints in the edge regions of the sampling pattern is smaller than acount of sampling points corresponding to the center region of thesampling pattern. Accordingly, the sampling density in the center regionof the sampling pattern may be greater than that in the edge regions ofthe sampling pattern.

In some embodiments, locations of sampling points on two or more blockswith the same size may be the same or different. As used herein, alocation of a sampling point on a block may be defined by a column and arow of the block. For example, a sampling point located on a firstcolumn of a first block may be located on a first row of the firstblock. A sampling point located on a first column of the second blockmay be located on a first row of the second block. It means thatlocations of the two sampling points on the first block and the secondblock are the same. As another example, a sampling point located on afirst column of a first block may be located on a first row of the firstblock. A sampling point located on a first row of a second block may belocated on a second row of the second block. It means that locations ofthe two sampling points on the first block and the second block aredifferent.

The count (number) of the plurality of blocks included in the samplingpattern may be set by a user or according to a default setting of theMRI system 100. For example, the count (number) of the plurality ofblocks may be determined based on the count (number) of the plurality ofconsecutive images included in the image sequence, the range of theplurality of phase encoding gradient field values, and the size of eachof the plurality of blocks. Taking the count of the plurality of imagesof P and the plurality of phase encoding gradient field values rangingin [−F, +F] as an example, if the plurality of blocks include the samesize of A×A, the count of the plurality of blocks (also referred to asN) may equal to 2F×P/A{circumflex over ( )}2.

In some embodiments, the sampling pattern associated with the imagesequence may include one or more segments, for example, a first segment,a second segment, and a third segment, etc. The first segment may referto a lower edge region of the sampling pattern. The second segment mayrefer to a center region of the sampling pattern. The third segment mayrefer to an upper edge region of the sampling pattern. Phase encodinggradient field values associated with the second segment may be smallerthan that associated with the first segment and greater than thatassociated with the third segment. For example, if the plurality ofphase encoding gradient field values associated with the image sequencerange from −128 to +128, phase encoding gradient field values associatedwith the first segment may range from +128 to +110, phase encodinggradient field values associated with the second segment may range from+110 to −110, and phase encoding gradient field values associated withthe third segment may range from −128 to −110. The first segment, thesecond segment and the third segment may include different samplingdensities (i.e., different counts of sampling points) or a same samplingdensity. For example, the sampling density in the first segment and thesampling density in the third segment may be smaller than the samplingdensity in the second segment. In other words, a count of samplingpoints in the first segment and the third segment may be smaller than acount of sampling points corresponding to the second segment.

Each of the first segment, the second segment, and the third segment mayinclude one portion of the plurality of blocks arranged along the timedimension, also referred to as a first portion of blocks, a secondportion of blocks and a third portion of blocks, respectively. Thesampling density of each of the first segment, the second segment, andthe third segment may be determined by the size of blocks in differentsegments. The greater the size of blocks in a segment is, the moresampling points in the segment may be, and the larger sampling densityof the segment may be. Thus, if the sampling density in the firstsegment and the sampling density in the third segment are smaller thanthe sampling density in the second segment, each block of the firstportion of blocks and each block of the third portion of blocks may havea smaller size (or include more points) than that of each block of thesecond portion of blocks. For example, each block of the second portionof blocks may be a 4×4 block, and each block of the first portion ofblocks and each block of the third portion of blocks may be a 9×9 block(as shown in FIG. 8). A size of 4×4 block is smaller than a size of 9×9block. The count of sampling points in 4×4 block is 4, and the count ofsampling points in 9×9 block is 9. The sampling density in a segmentwith multiple 4×4 blocks are greater than the sampling density in asegment with multiple 9×9 blocks. In some embodiments, the size (orpoints) of each block of the first portion of blocks may be differentfrom that of each block of the third portion of blocks. For example,each block of the first portion of blocks may be a 4×4 block, each blockof the third portion of blocks may be a 6×6 block, and each block of thethird portion of blocks may be a 9×9 block. In some embodiments, thesampling density in different regions of the first segment, the secondsegment, and/or the third segment may be different. For example, thefirst portion of blocks, the second portion of blocks, and/or the thirdportion of blocks may include different sizes of blocks. For example,the first segment may include the first portion of blocks including 4×4blocks and 6×6 blocks. A region in the first segment with 4×4 blocks mayhave a greater sampling density than a region in the first segment with6×6 blocks. As another example, the second segment may include thesecond portion of blocks including 3×3 blocks and 2×2 blocks. A regionin the second segment with 3×3 blocks may have a smaller samplingdensity than a region in the second segment with 2×2 blocks.

In some embodiments, the sampling pattern may be determined based on apreliminary sampling pattern associated with the image sequence. Thepreliminary sampling pattern may be defined by a phase encodingdimension and a time dimension similar with the description of thesampling pattern elsewhere of the present disclosure. The samplingpattern determination module 404 may classify the preliminary samplingpattern into one or more segments (e.g., the first segment, the secondsegment and the third segments) along the phase encoding dimension. Thesampling pattern determination module 404 may classify each of the oneor more segments into a plurality of blocks along the time dimension.Each of the plurality of blocks may be associated with a plurality ofphase encoding gradient field values. A plurality of sampling points maybe determined for each of the plurality of blocks in each of the one ormore segments. Each of the plurality of phase encoding gradient fieldvalues may correspond to one single sampling point denoting a k-spaceline. A target sampling pattern may be determined including theplurality of sampling points in the each of the plurality of blocks foreach of the one or more segments. Details (e.g., size, or locations ofsampling points) of determining the sampling pattern may be foundelsewhere in the present disclosure (e.g., FIG. 6 and the descriptionsthereof).

In 504, k-space data associated with the image sequence using thesampling pattern may be obtained. Operation 504 may be performed by theacquisition module 402. The k-space data may include a plurality of subk-space data arranged in time sequence corresponding to the plurality ofimages included in the image sequence. In some embodiments, the subk-space data associated with each of the plurality of images the imagesequence may be determined by filling a k-space with MR signalsassociated with each of the plurality of consecutive images along aplurality of readout lines. Each of the plurality readout lines may besampled or filled just only once during a time period (e.g., the severalsub-time periods).

In some embodiments, MR signals associated with a specific image in theimage sequence (or sub k-space data) may be acquired based on samplingpoints in the sampling pattern corresponding to the specific image. Forexample, the MR signals associated with the specific image may beacquired by applying specific phase encoding gradient field values atspecific time values according to the sampling points in the samplingpattern. In some embodiments, the MR signals associated with thespecific image in the image sequence may be collected by an MR scanner(e.g., the MR scanner 110) according to one or more pulse sequences. Theone or more pulse sequences may be designed based on the samplingpattern. For example, the sampling pattern may be stored as a lookuptable including a plurality of phase encoding gradient field values andarrangement thereof in time sequence. A time for applying a phaseencoding gradient field and/or an intensity for applying a phaseencoding gradient field may be determined according to the lookup table.

In some embodiments, the arrangement of the plurality of phase encodinggradient field values in time sequence may be determined by identifyinga plurality of sampling trajectories corresponding to the plurality ofconsecutive images in the image sequence from the sampling pattern. Eachof the plurality of sampling trajectories may correspond to one of theplurality of consecutive images. The lookup table may denote theplurality of sampling trajectories corresponding to the plurality ofconsecutive images. A sampling trajectory may be defined by one or moresampling points corresponding to an image arranged in time sequence,also referred as a direction of the sampling trajectory. A direction ofa sampling trajectory may be defined by a change of phase encodinggradient field values corresponding to the one or more sampling points.For example, the phase encoding gradient field values corresponding tothe plurality of sampling points on at least one of the plurality ofsampling trajectories may vary in a descending order (i.e., descendingdirection) or an ascending order (i.e., ascending direction). Taking theascending order as an example, a phase encoding gradient field valuecorresponding to a first sampling point on a sampling trajectory may besmaller than a phase encoding gradient field value corresponding to asecond sampling point on the sampling trajectory. The second samplingpoint and the first sampling point may be two consecutive samplingpoints on the sampling trajectory. Directions of sampling trajectorieswith respect to two neighboring images in the sampling pattern may bethe same or different. Taking different directions of samplingtrajectories with respect to two neighboring images (e.g., a firstimage, a second image) as an example, if the sampling trajectory withrespect to the first image includes a descending direction, the samplingtrajectory with respect to the second image may include an ascendingdirection. The sampling trajectories with respect to two neighboringimages may share a sampling point at a phase coding gradient fieldvalue. For example, when the directions of sampling trajectories withrespect to two neighboring images in the sampling pattern are inverse,the sampling trajectories with respect to two neighboring images mayshare a sampling point at a maximum or minimum phase coding gradientfield value associated with the two neighboring images. As a furtherexample, if the sampling trajectory with respect to the first imageincludes a descending direction and the sampling trajectory with respectto the second image s includes an ascending direction, the samplingtrajectories with respect to the first image and the second image mayshare a sampling point at the minimum phase encoding gradient fieldvalue associated with the sampling trajectory with respect to the firstimage or the second image. If the sampling trajectory with respect tothe first image includes an ascending direction and the samplingtrajectory with respect to the second image may include a descendingdirection, the sampling trajectories with respect to the first image andthe second image may share a sampling point at the maximum phaseencoding gradient field value associated with the sampling trajectorywith respect to the first image or the second image.

In 506, the image sequence may be reconstructed based on the k-spacedata. Operation 506 may be performed by the image reconstruction module406. In some embodiments, the k-space data may include a plurality ofsub k-space data arranged in time sequence corresponding to theplurality of images included in the image sequence. Each of theplurality of images may be reconstructed based on corresponding subk-space data using an MR image reconstruction technique. Exemplary MRimage reconstruction techniques may include a 2-dimensional Fouriertransform technique, a back projection technique (e.g., a convolutionback projection technique, a filtered back projection technique), aniteration reconstruction technique, etc. Exemplary iterationreconstruction techniques may include an algebraic reconstructiontechnique (ART), a simultaneous iterative reconstruction technique(SIRT), a simultaneous algebraic reconstruction technique (SART), anadaptive statistical iterative reconstruction (ASIR) technique, amodel-based iterative reconstruction (MBIR) technique, a sinogramaffirmed iterative reconstruction (SAFIR) technique, or the like, or anycombination thereof. In some embodiments, a reference readout line maybe determined by weighting and/or averaging the sub k-space datacorresponding to k-space lines associated with each of the plurality ofconsecutive images along the time dimension. The reference readout linemay correspond to reference k-space data that may be used forreconstructing a reference image based on an MR image reconstructiontechnique. The plurality of consecutive images may be reconstructedbased on the reference image using, for example an iterationreconstruction technique.

It should be noted that the above description is merely provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. For persons having ordinary skills in the art,multiple variations or modifications may be made under the teachings ofthe present disclosure. However, those variations and modifications donot depart from the scope of the present disclosure.

FIG. 6 is a flowchart illustrates an exemplary process forreconstructing an image sequence according to a sampling patternaccording to some embodiments of the present disclosure. In someembodiments, one or more operations of process 600 illustrated in FIG. 6may be implemented in the MRI system 100 illustrated in FIG. 1. Forexample, process 600 illustrated in FIG. 6 may be stored in the storagedevice 130 in the form of instructions, and invoked and/or executed bythe processing device 120 (e.g., the processor 210 of the computingdevice 200 as illustrated in FIG. 2, the GPU 330 or CPU 340 of themobile device 300 as illustrated in FIG. 3).

In 602, one or more blocks associated with a plurality of phase encodinggradient field values may be determined. Operation 602 may be performedby the sampling pattern determination module 404. A block may have asize N×M. N may refer to a count of rows in the block. Each of the rowsin the block may correspond to a phase encoding gradient field value. Mmay refer to a count of columns in the block. Each of the columns in theblock may correspond to an image in an image sequence as describedelsewhere in the present disclosure (e.g., FIG. 5 and the descriptionsthereof). In some embodiments, N may be equal to M. For example, the oneor more blocks may include a 4×4 block, 6×6 block, 9×9 block, etc. Thesize of a block may be defined by the number or count of the pluralityof points in the block. For example, a block with size of 4×4 (alsoreferred to as a 4×4 block, e.g., a block 702 (A) or a block 704 (B) asshown in FIG. 7) may include 16 points. A block with size of 9×9 (alsoreferred to as a 9×9 block, e.g., a block 706 (C) or a block 708 (D) asshown in FIG. 7) may have 81 points. Different sizes of the block may beassociated with different counts (numbers) of phase encoding gradientfield values, and include different counts (numbers) of the plurality ofpoints. For example, a 4×4 block (e.g., a block 702 (A) or a block 704(B) as shown in FIG. 7) may be associated with 4 phase encoding gradientfield values, and include 16 points. As another example, a 9×9 block(e.g., a block 706 (C) or a block 708 (D) as shown in FIG. 7) may beassociated with 9 phase encoding gradient field values, and include 81points. More descriptions of the blocks may be found elsewhere in thepresent disclosure, e.g., FIG. 5 and the descriptions thereof.

In 604, a pre-determined number of sampling points may be determinedfrom each of the one or more blocks. Operation 604 may be performed bythe sampling pattern determination module 404.

In some embodiments, a number (count) of the one or more blocks may bearranged in a two-dimensional space defined by the phase encodingdimension and the time dimension. Each of the one or more blocks mayinclude the pre-determined number of sampling points associated with atime period and one portion of a plurality of phase encoding gradientfield values associated with an image sequence. For example, if theplurality of phase encoding gradient field values associated with theimage sequence range from −128 to +128, a block may be associated withthe one portion of the plurality of phase encoding gradient field valuesranging from +128 to +120, +128 to +120, −128 to −120, etc. A timeperiod associated with a block may be classified into multiple sub-timeperiods. Positions of sampling points in a block may need to satisfy thecriterion. The criterion may be that each of phase encoding gradientfield values associated with a block may correspond to one singlesampling point and each of the multiple sub-time periods associated withthe block may correspond to one single sampling point. In other words, ablock herein may be denoted as a square array including a plurality ofpoints (or squares) arranged in a plurality of columns and rows. Onesingle point in each of the plurality of columns of the block may bedetermined as a sampling point. One single point in each of theplurality of rows of the block may be determined as a sampling point.Sampling points in each of the plurality of columns of the block may belocated in different rows of the block. In other words, the samplingpoints in the block may occupy different columns and different rows. Forexample, if a position of each of the sampling points in the block isdenoted by coordinates including a horizontal coordinate (e.g., a phaseencoding gradient value) and a vertical coordinate (e.g., a time valueor a sequence number of an image), horizontal coordinates of each of thesampling points are different and the vertical coordinates of each ofthe sampling points are different. Accordingly, when a specific phaseencoding gradient field value is applied for collecting an MR signal,the specific phase encoding gradient field value may be not applied forcollecting MR signals during a time period (e.g., such as 200 ms, 300ms, 400 ms, etc.) associated with a block. The sampling points in ablock may be determined by a user or according to a default setting ofthe MRI system 100. For example, the user may select the sampling pointsfrom the block manually. As another example, the processing device 120may select the sampling points from the block randomly. Thepre-determined number of sampling points on a block may relate to thesize of the block. The greater the size of the block is, the greater thecount of sampling points on the block may be. More descriptions of thesampling points may be found elsewhere in the present disclosure, e.g.,operation 502 and the descriptions thereof.

In 606, a sampling pattern associated with an image sequence may bedetermined based on the plurality of sampling points in each of the oneor more of blocks. Operation 606 may be performed by the samplingpattern determination module 404. The sampling pattern may have avertical axis denoting a phase encoding dimension, and a horizontal axisdenoting a time dimension. In some embodiments, the sampling pattern maybe determined based on the one or more blocks with sampling pointsdetermined in operation 604. In some embodiments, the one or more blocksmay have the same size. Locations of sampling points in the one or moreblocks may be the same or different. The sampling pattern may bedetermined by combining a number or count of the one or more blocks withthe same size (e.g., 4×4 block). For example, the number or count of theone or more blocks with the same size having sampling points may bearranged along the phase encoding dimension and the time dimension toform a sampling pattern. In some embodiments, the one or more blocks mayhave different sizes. For example, the one or more blocks may include afirst block with a first size (e.g., 9×9 block), a second block with asecond size (e.g., 4×4 block), and a third block with a third size(e.g., 9×9 block). The first size and/or the third size may be largerthan the second size. A first count of first blocks may be arrangedalong the time dimension to form a first segment of the samplingpattern. A second count of second blocks may be arranged along the timedimension to form a second segment of the sampling pattern. A thirdcount of third blocks may be arranged along the time dimension to form athird segment of the sampling pattern. Locations of sampling points onthe first blocks, the second blocks, the third blocks may be the same ordifferent. In some embodiments, the count of blocks and arrangements ofthe blocks in the sampling pattern may depend on the count of theplurality of images in the image sequence and the size of each of theplurality of images. More descriptions of the sampling pattern may befound elsewhere in the present disclosure, e.g., operation 502 and thedescriptions thereof.

In 608, k-space data associated with the image sequence using thesampling pattern may be obtained. Operation 608 may be performed by theacquisition module 402. Operation 608 may be the same as or similar withoperation 504, and may not repeated here.

In 610, the image sequence may be reconstructed based on the k-spacedata. Operation 610 may be performed by the image reconstruction module406. Operation 610 may be the same as or similar with operation 504, andmay not repeated here.

It should be noted that the above description is merely provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. For persons having ordinary skills in the art,multiple variations or modifications may be made under the teachings ofthe present disclosure. However, those variations and modifications donot depart from the scope of the present disclosure.

FIG. 7 is a schematic diagram illustrating exemplary blocks according tosome embodiments of the present disclosure. As shown in FIG. 7, a block702 (also referred as block A), a block 704 (also referred as block B),a block 706 (also referred as block C, and a block 708 (also referred asblock D) may be illustrated. Each of the blocks may include a pluralityof points. Each of the plurality of points may correspond to a phaseencoding gradient field value in a phase direction (i.e., ky directionas shown in FIG. 7) and a time or period in the time dimension (i.e., tdirection as shown in FIG. 7). Each of the blocks may include aplurality of sampling points denoted by block spots in FIG. 7. Eachspecific phase encoding gradient field value associated with a block maycorrespond to one single sampling point and each time or periodassociated with the block may correspond to one single sampling point.In other words, there is no a sampling point in a block corresponding tothe same ky value (i.e., phase encoding gradient field value) or thesame t value (i.e., time period). Accordingly, a specific phase encodinggradient field value associated with a block may be not applied forcollecting MR signals repeatedly during a time period (e.g., such as 200ms, 300 ms, 400 ms, etc.) associated with the block.

Block A and block B have the same size, both being a 4×4 block including16 points and 4 sampling points. Each of columns of Block A and Block Bincludes one single sampling point and each of rows of Block A and BlockB includes one single sampling point. Sampling points in Block A orBlock B are located on different columns and rows. In other words, eachof the sampling points in Block A or Block B corresponds to different kyvalues and t values. Accordingly, a position of a k-space may be sampledjust once during a time period associated with Block A or Block B.Locations of sampling points on block A and locations of sampling pointson block B are different. For example, a sampling point s1 on block A islocated on the first row and the first column. A sampling point s1 onblock B is located on the second row and the first column.

Block C and block D have the same size, both being a 9×9 block including81 points and 9 sampling points. Each of columns of Block C and Block Dincludes one single sampling point and each of rows of Block C and BlockD includes one single sampling point. Sampling points in Block C orBlock D are located on different columns and rows. In other words, eachof the sampling points in Block C or Block D corresponds to different kyvalues and t values. Accordingly, a position of a k-space may be sampledjust once during a time period associated with Block C or Block D.Locations of sampling points on block C and locations of sampling pointson block D are different. For example, a sampling point s2 on block C islocated on the third row and the second column. A sampling point s2 onblock D is located on the first row and the second column.

FIG. 8 is a schematic diagram illustrating an exemplary sampling patternaccording to some embodiments of the present disclosure. The horizontalaxis refers a time dimension denoting consecutive images in an imagesequence. The vertical axis refers a phase encoding dimension denotingphase encoding gradient field values. The sampling pattern 800 may beassociated with an image sequence including 36 images with an image sizeof 30×30. The plurality of phase encoding gradient field values mayrange from −15 to +15. It should be noted that the plurality of phaseencoding gradient field values ranging from −15 to +15 may be oneportion of phase encoding gradient field values associated with theimage sequence.

As shown in FIG. 8, the sampling pattern 800 includes a first segment810, a second segment 820, and a third segment 830. The first segment810 and third segment 830 are filled by four 9×9 blocks respectivelyarranged along the time dimension. Locations of sampling points in thefour 9×9 blocks in the first segment 810 are the same. Locations ofsampling points in the four 9×9 blocks in the third segment 830 are thesame. Locations of sampling points in a 9×9 block in the first segment810 and locations of sampling points in a 9×9 blocks in the thirdsegment 830 are different. The second segment 820 are filled bytwenty-seven 4×4 blocks. The second segment 820 includes three parts.Each of the three parts includes nine 4×4 blocks arranged along the timedimension. Locations of sampling points on in each of the three parts ofthe second segment are different. Each of columns of blocks in each ofthe three parts of the second segment includes one single sampling pointand each of rows of blocks in each of the three parts of the secondsegment includes one single sampling point. Sampling points in blocks ineach of the three parts of the second segment are located on differentcolumns and rows. In other words, each of the sampling points in blocksin each of the three parts of the second segment corresponds todifferent ky values and t values. Accordingly, a position of a k-spacemay be sampled just once during a time period associated with a block ineach of the three parts of the second segment.

The sampling density corresponding to the first segment 810 and thethird segment 830 are smaller than the sampling density correspond tothe second segment 820 as blocks in the second segment has a greatersize than blocks in the first segment and the third segment. Each of the36 consecutive images corresponds to 5 sampling points denoting 5readout lines.

FIG. 9 is a schematic diagram illustrating exemplary samplingtrajectories associated with one portion of an image sequence accordingto some embodiments of the present disclosure. As shown in FIG. 9, thehorizontal axis refers to a time dimension representing one or moreconsecutive images in an image sequence. The vertical axis refers to aphase encoding dimension representing one or more phase encodinggradient field values. A black spot as shown in FIG. 9 indicates asampling point with a specific time values and a specific phase encodinggradient field values identified from the sampling pattern. FIG. 8illustrated sampling points corresponding to each image in the imagesequence. FIG. 9 shows an arrangement of the sampling points in timesequence corresponding to each image, also referred to as a samplingtrajectory associated with each image. A plurality of samplingtrajectories associated with consecutive images (e.g., images 1-6 shownin FIG. 9) may be identified from the sampling pattern as shown in FIG.8. Two neighboring images may be represented by a rectangle with solidlines and a rectangle with dotted lines respectively. For illustration,FIG. 9 shows one portion of the plurality of sampling trajectories andone portion of consecutive images (e.g., images 1-6 shown in FIG. 9) inan image sequence.

As shown in FIG. 9, each of the 6 consecutive images includes fivesampling points. Any two neighboring images have different directions ofsampling trajectories and share a sampling point. Taking Image 1, Image2 and Image 3 as an example, Image 1 and Image 3 are both neighboringimages of Image 2. Phase encoding gradient field values corresponding tosampling points of Image 1 change in a descending order along the timedimension such that the sampling trajectory with respect to Image 1 hasa descending direction (denoted by arrow A in FIG. 9). Phase encodinggradient field values corresponding to sampling points of Image 2 changein an ascending order along the time dimension such that the samplingtrajectory with respect to Image 2 has an ascending direction (denotedby arrow B in FIG. 9). Similar with Image 1, the sampling trajectorywith respect to Image 3 has a descending direction. Image 1 and Image 2share a sampling point at a phase coding gradient field value with theminimum value (e.g., at −15) of phase coding gradient field valuesassociated with Image 1 and Image 2. Image 2 and Image 3 share asampling point at a phase coding gradient field value with the maximumvalue (e.g., at +15) of phase coding gradient field values associatedwith Image 2 and Image 3.

It should be noted that the above description is merely provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. For persons having ordinary skills in the art,multiple variations or modifications may be made under the teachings ofthe present disclosure. However, those variations and modifications donot depart from the scope of the present disclosure.

Having thus described the basic concepts, it may be rather apparent tothose skilled in the art after reading this detailed disclosure that theforegoing detailed disclosure is intended to be presented by way ofexample only and is not limiting. Various alterations, improvements, andmodifications may occur and are intended to those skilled in the art,though not expressly stated herein. These alterations, improvements, andmodifications are intended to be suggested by this disclosure, and arewithin the spirit and scope of the exemplary embodiments of thisdisclosure.

Moreover, certain terminology has been used to describe embodiments ofthe present disclosure. For example, the terms “one embodiment,” “anembodiment,” and/or “some embodiments” mean that a particular feature,structure or characteristic described in connection with the embodimentis included in at least one embodiment of the present disclosure.Therefore, it is emphasized and should be appreciated that two or morereferences to “an embodiment” or “one embodiment” or “an alternativeembodiment” in various portions of this specification are notnecessarily all referring to the same embodiment. Furthermore, theparticular features, structures or characteristics may be combined assuitable in one or more embodiments of the present disclosure.

Further, it will be appreciated by one skilled in the art, aspects ofthe present disclosure may be illustrated and described herein in any ofa number of patentable classes or context including any new and usefulprocess, machine, manufacture, or composition of matter, or any new anduseful improvement thereof. Accordingly, aspects of the presentdisclosure may be implemented entirely hardware, entirely software(including firmware, resident software, micro-code, etc.) or combiningsoftware and hardware implementation that may all generally be referredto herein as a “unit,” “module,” or “system.” Furthermore, aspects ofthe present disclosure may take the form of a computer program productembodied in one or more computer-readable media having computer readableprogram code embodied thereon.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including electromagnetic, optical, or thelike, or any suitable combination thereof. A computer readable signalmedium may be any computer readable medium that is not a computerreadable storage medium and that may communicate, propagate, ortransport a program for use by or in connection with an instructionexecution system, apparatus, or device. Program code embodied on acomputer readable signal medium may be transmitted using any appropriatemedium, including wireless, wireline, optical fiber cable, RF, or thelike, or any suitable combination of the foregoing.

Computer program code for carrying out operations for aspects of thepresent disclosure may be written in any combination of one or moreprogramming languages, including an object-oriented programming languagesuch as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C #, VB.NET, Python or the like, conventional procedural programming languages,such as the “C” programming language, Visual Basic, Fortran 2103, Perl,COBOL 2102, PHP, ABAP, dynamic programming languages such as Python,Ruby, and Groovy, or other programming languages. The program code mayexecute entirely on the user's computer, partly on the user's computer,as a stand-alone software package, partly on the user's computer andpartly on a remote computer or entirely on the remote computer orserver. In the latter scenario, the remote computer may be connected tothe user's computer through any type of network, including a local areanetwork (LAN) or a wide area network (WAN), or the connection may bemade to an external computer (for example, through the Internet using anInternet Service Provider) or in a cloud computing environment oroffered as a service such as a Software as a Service (SaaS).

Furthermore, the recited order of processing elements or sequences, orthe use of numbers, letters, or other designations, therefore, is notintended to limit the claimed processes and methods to any order exceptas may be specified in the claims. Although the above disclosurediscusses through various examples what is currently considered to be avariety of useful embodiments of the disclosure, it is to be understoodthat such detail is solely for that purpose, and that the appendedclaims are not limited to the disclosed embodiments, but, on thecontrary, are intended to cover modifications and equivalentarrangements that are within the spirit and scope of the disclosedembodiments. For example, although the implementation of variouscomponents described above may be embodied in a hardware device, it mayalso be implemented as a software-only solution, for example, aninstallation on an existing server or mobile device.

Similarly, it should be appreciated that in the foregoing description ofembodiments of the present disclosure, various features are sometimesgrouped together in a single embodiment, figure, or description thereoffor the purpose of streamlining the disclosure aiding in theunderstanding of one or more of the various inventive embodiments. Thismethod of disclosure, however, is not to be interpreted as reflecting anintention that the claimed subject matter requires more features thanare expressly recited in each claim. Rather, inventive embodiments liein less than all features of a single foregoing disclosed embodiment.

In some embodiments, the numbers expressing quantities or propertiesused to describe and claim certain embodiments of the application are tobe understood as being modified in some instances by the term “about,”“approximate,” or “substantially.” For example, “about,” “approximate,”or “substantially” may indicate ±20% variation of the value itdescribes, unless otherwise stated. Accordingly, in some embodiments,the numerical parameters set forth in the written description andattached claims are approximations that may vary depending upon thedesired properties sought to be obtained by a particular embodiment. Insome embodiments, the numerical parameters should be construed in lightof the number of reported significant digits and by applying ordinaryrounding techniques. Notwithstanding that the numerical ranges andparameters setting forth the broad scope of some embodiments of theapplication are approximations, the numerical values set forth in thespecific examples are reported as precisely as practicable.

Each of the patents, patent applications, publications of patentapplications, and other material, such as articles, books,specifications, publications, documents, things, and/or the like,referenced herein is hereby incorporated herein by this reference in itsentirety for all purposes, excepting any prosecution file historyassociated with same, any of same that is inconsistent with or inconflict with the present document, or any of same that may have alimiting affect as to the broadest scope of the claims now or laterassociated with the present document. By way of example, should there beany inconsistency or conflict between the description, definition,and/or the use of a term associated with any of the incorporatedmaterial and that associated with the present document, the description,definition, and/or the use of the term in the present document shallprevail.

In closing, it is to be understood that the embodiments of theapplication disclosed herein are illustrative of the principles of theembodiments of the application. Other modifications that may be employedmay be within the scope of the application. Thus, by way of example, butnot of limitation, alternative configurations of the embodiments of theapplication may be utilized in accordance with the teachings herein.Accordingly, embodiments of the present application are not limited tothat precisely as shown and described.

1-20. (canceled)
 21. A system for determining a sampling pattern formagnetic resonance imaging (MRI), comprising: at least one storagedevice storing executable instructions, and at least one processor incommunication with the at least one storage device, wherein whenexecuting the executable instructions, the at least one processor causesthe system to perform operations including: determining one or moreblocks associated with a plurality of phase encoding (PE) gradient fieldvalues, each of the one or more blocks including a plurality of pointsarranged in a plurality of columns and rows; determining a plurality ofsampling points from each of the one or more blocks, wherein theplurality of sampling points in each of the one or more blocks occupydifferent points of the corresponding block; and determining thesampling pattern associated with an image sequence based on theplurality of sampling points in each of the one or more blocks, whereinthe sampling pattern is used to acquire encoded MR signals forreconstructing a plurality of images in the image sequence.
 22. Thesystem of claim 21, wherein the each of the one or more blocks includesa plurality of points, and the determining a plurality of samplingpoints from each of the one or more blocks comprises: receiving a userinstruction; and selecting, based on the user instruction and from theplurality of points in each of the one or more blocks, the plurality ofsampling points.
 23. The system of claim 21, wherein the each of the oneor more blocks includes a plurality of points, and the determining aplurality of sampling points from each of the one or more blockscomprises: selecting, from the plurality of points in each of the one ormore blocks, the plurality of sampling points randomly.
 24. The systemof claim 21, wherein the one or more blocks includes a first block and asecond block of different sizes and/or different counts of samplepoints.
 25. The system of claim 21, wherein the one or more blocks areof a same size, and the determining the sampling pattern associated withan image sequence based on the plurality of sampling points in each ofthe one or more blocks comprises: arranging the one or more blocks alonga first dimension and a second dimension to form the sampling pattern,wherein the first dimension is perpendicular to the second dimension.26. The system of claim 21, wherein the one or more blocks includes afirst count of first blocks of a first size and a second count of secondblocks of a second size, and the determining the sampling patternassociated with an image sequence based on the plurality of samplingpoints in each of the one or more blocks comprises: arranging the firstcount of first blocks along a first dimension to form a first segment;arranging the second count of second blocks along the first dimension toform a second segment; and combining the first segment and the secondsegment along a second dimension to form the sampling pattern, whereinthe second dimension is perpendicular to the second dimension.
 27. Thesystem of claim 26, wherein the arrangement of the first blocks in thefirst segment or the arrangement of the second blocks in the secondsegment is periodic.
 28. The system of claim 21, wherein the at leastone processor further causes the system to perform the operationsincluding: identifying a plurality of sampling trajectories from thesampling pattern, each of the plurality of sampling trajectoriescorresponding to one of the plurality of images, each of the pluralityof sampling trajectories including a plurality of sampling pointsarranged in a time sequence.
 29. The system of claim 28, wherein a countof the plurality of sampling points on each of the plurality of samplingtrajectories is the same.
 30. The system of claim 29, wherein phaseencoding gradient field values corresponding to the plurality ofsampling points on at least one of the plurality of samplingtrajectories vary in a descending order or an ascending order.
 31. Thesystem of claim 30, wherein directions of sampling trajectories withrespect to two neighboring images of the image sequence are different,wherein a direction of a sampling trajectory is defined by a change ofphase encoding gradient field values corresponding to the plurality ofsampling points.
 32. The system of claim 31, wherein the samplingtrajectories with respect to the two neighboring images of the imagesequence share a sampling point at a phase coding gradient field value,wherein the phase coding gradient field value is a maximum or minimum ofthe plurality of phase encoding gradient field values associated withthe sampling pattern.
 33. The system of claim 28, wherein the at leastone processor further causes the system to perform the operationsincluding: determining a pulse sequence based on the plurality ofsampling trajectories; collecting the encoded MR signals based on thepulse sequence; determining k-space data associated with the imagesequence based on the encoded MR signals; and reconstructing the imagesequence based on the k-space data.
 34. The system of claim 33, whereinthe at least one processor further causes the system to perform theoperations including: averaging at least a portion of the k-space dataalong a time dimension of the sampling pattern to obtain referencek-space data associated with the image sequence; and reconstructing theimage sequence based on the reference k-space data.
 35. A method fordetermining a sampling pattern for magnetic resonance imaging (MRI),implemented on a computing device including at least one processor, atleast one storage device, and a communication platform connected to anetwork, comprising: determining one or more blocks associated with aplurality of phase encoding (PE) gradient field values, each of the oneor more blocks including a plurality of points arranged in a pluralityof columns and rows; determining a plurality of sampling points fromeach of the one or more blocks, wherein the plurality of sampling pointsin each of the one or more blocks occupy different points of thecorresponding block; and determining the sampling pattern associatedwith an image sequence based on the plurality of sampling points in eachof the one or more blocks, wherein the sampling pattern is used toacquire encoded MR signals for reconstructing a plurality of images inthe image sequence.
 36. The method of claim 35, wherein the one or moreblocks includes a first block and a second block of different sizesand/or different counts of sample points.
 37. The method of claim 35,wherein the one or more blocks are of a same size, and the determiningthe sampling pattern associated with an image sequence based on theplurality of sampling points in each of the one or more blockscomprises: arranging the one or more blocks along a first dimension anda second dimension to form the sampling pattern, wherein the firstdimension is perpendicular to the second dimension.
 38. The method ofclaim 35, wherein the one or more blocks includes a first count of firstblocks of a first size and a second count of second blocks of a secondsize, and the determining the sampling pattern associated with an imagesequence based on the plurality of sampling points in each of the one ormore blocks comprises: arranging the first count of first blocks along afirst dimension to form a first segment; arranging the second count ofsecond blocks along the first dimension to form a second segment; andcombining the first segment and the second segment along a seconddimension to form the sampling pattern, wherein the second dimension isperpendicular to the second dimension.
 39. The method of claim 35,further comprising: identifying a plurality of sampling trajectoriesfrom the sampling pattern, each of the plurality of samplingtrajectories corresponding to one of the plurality of images, each ofthe plurality of sampling trajectories including a plurality of samplingpoints arranged in a time sequence.
 40. A non-transitorycomputer-readable storage medium, comprising at least one set ofinstructions for determining a sampling pattern for magnetic resonanceimaging (MRI), wherein when executed by at least one processor of acomputing device, the at least one set of instructions direct the atleast one processor to perform acts of: determining one or more blocksassociated with a plurality of phase encoding (PE) gradient fieldvalues, each of the one or more blocks including a plurality of pointsarranged in a plurality of columns and rows; determining a plurality ofsampling points from each of the one or more blocks, wherein theplurality of sampling points in each of the one or more blocks occupydifferent points of the corresponding block; and determining thesampling pattern associated with an image sequence based on theplurality of sampling points in each of the one or more blocks, whereinthe sampling pattern is used to acquire encoded MR signals forreconstructing a plurality of images in the image sequence.